Applied Research Advances

Vol. 2 No. 1 (2026): Applied Research Advances
Articles

An Integrated Bipolar pqr-Spherical Fuzzy Decision Framework for the Selection of AI-driven Security System

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  • Kannusamy Aarthi,
  • Samayan Narayanamoorthy,
  • Krishnan Suvitha
Kannusamy Aarthi
Department of Mathematics, Bharathiar University, Coimbatore, India
Samayan Narayanamoorthy
Graduate School of Technology and Innovation Management, Daegu Gyeongbuk Institute of Science & Technology, Hyeonpung-Eup, Dalseong-Gun Daegu, Republic of Korea
Krishnan Suvitha
Centre for Nonlinear Systems, Chennai Institute of Technology, Chennai, India

Published 2026-07-11

Keywords

  • Security system,
  • AI-Analytics,
  • Bipolar pqr-spherical fuzzy sets,
  • ALWAS,
  • Smart city

Copyright (c) 2026 Kannusamy Aarthi, Samayan Narayanamoorthy, Krishnan Suvitha (Author)

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Aarthi, K., Narayanamoorthy, S., & Suvitha, K. (2026). An Integrated Bipolar pqr-Spherical Fuzzy Decision Framework for the Selection of AI-driven Security System. Applied Research Advances, 2(1), 179-198. https://doi.org/10.65069/ara21202626
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Abstract

With rising urbanization and urban population growth, enhancing public safety has become a significant component of sustainable smart city development. Therefore, it is essential to select a reliable, adaptive, and effective security system to ensure a safe urban environment. The implementation of artificial intelligence in smart cities offers a promising solution to address these challenges. However, selecting a security system that enhances public safety while ensuring privacy and sustainability is often a challenging task, as its implementation requires a meticulous evaluation of all relevant criteria. Under these circumstances, the need for multi-criteria decision-making arises. In this regard, this study employs an integrated decision-making framework that integrates the Symmetry Point of Criterion (SPC) approach with the Aczel–Alsina Weighted Assessment (ALWAS) technique under a bipolar pqr-spherical fuzzy environment to select the optimal AI-driven security system. The study considers four security systems as potential alternatives, evaluated based on ten criteria categorized into technical, economic and environmental, and social dimensions. The assessment data are expressed using bipolar pqr-spherical fuzzy sets, while the weights of the criteria are determined using the SPC approach, and the alternatives are ranked using the ALWAS technique. Furthermore, the consistency and stability of the results are validated through comparative analysis and sensitivity analysis, respectively.

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